Model-based Signal Processing Research Articles

SEPARATION OF ARTERIAL PRESSURE INTO SOLITARY WAVES AND WINDKESSEL FLOW

A simplified model of arterial blood pressure intended for use in model-based signal processing applications is presented. The main idea is to decompose the pressure into two components: a travelling wave describes the fast propagation phenomena predominating during the systolic phase and a windkessel flow represents the slow phenomena during the diastolic phase. Instead of decomposing the blood pressure pulse into a linear superposition of forward and backward harmonic waves, as in the linear wave theory, a nonlinear superposition of travelling waves matched to a reduced physical model of the pressure, is proposed. Very satisfactory experimental results are obtained by using forward waves, the N-soliton solutions of a Korteweg-de Vries equation in conjunction with a two-element windkessel model. The parameter identifiability in the practically important 3-soliton case is also studied. The proposed approach is briefly compared with the linear one and its possible clinical relevance is discussed.

Sensing of unexploded ordnance with magnetometer and induction data: theory and signal processing

We consider the detection of subsurface unexploded ordnance via magnetometer and electromagnetic-induction (EMI) sensors. Detection performance is presented, using model-based signal processing algorithms. We first develop and validate the parametric models, using both numerical and measured data. These models are then applied in the context of feature extraction, and the features are processed via two signal-processing algorithms. The detection algorithms are discussed in detail, with comparisons made based on performance with measured magnetometer and EMI data.

Effects of non-nutritive sucking on heart rate, respiration and oxygenation: a model-based signal processing approach

Several studies support the idea that the use of pacifiers can reduce the risk of Sudden Infant Death Syndrome. To investigate the effect of non-nutritive sucking (NNS), we measured heart rate, abdominal respiration, EMG and arterial oxygen saturation of 20 neonates. Also, in 10 of these neonates, changes in cerebral hemoglobin concentrations were acquired by means of near-infrared spectroscopy. Using a parametric technique to model the heart rate as a sum of exponentially damped sinusoids, two main frequency components were found in the heart rate during NNS: a frequency of approximately 0.08 Hz due to the alternation of sucking bursts and pauses, and a frequency of approximately 0.8 Hz that reflects the influence of the respiration. Our analysis shows that it is the alternation of bursts and pauses itself that causes the increased heart rate variability, and that this is not due to increased effort. This suggests that the neuronal mechanism regulating NNS also stimulates the heart rate. From our measurements, no effect of NNS on cerebral or peripheral oxygenation could be found. Furthermore, we show that our model-based signal processing technique is well suited for the analysis of non-stationary biomedical signals.

An Overview of New Techniques and Effects in Model-based Sound Synthesis

Physical modeling and model-based sound synthesis have recently been among the most active topics of computer music and audio research. In the modeling approach one typically tries to simulate and duplicate the most prominent sound generation properties of the acoustic musical instrument under study. If desired, the models developed may then be modified in order to create sounds that are not common or even possible from physically realizable instruments. In addition to physically related principles it is possible to combine physical models with other synthesis and signal processing methods to realize hybrid modeling techniques. This article gives an overview of some recent results in model-based sound synthesis and related signal processing techniques. The focus is on modeling and synthesizing plucked string sounds, although the techniques may find much more widespread application. First, as a background, an advanced linear model of the acoustic guitar is discussed along with model control principles. Then the methodology to include inherent nonlinearities and time-varying features is introduced. Examples of nonlinearities are studied in the context of two string instruments, the kantele and the tanbur, which exhibit interesting nonlinear effects.

A capacitive pressure sensor interface using oversampling Δ-Σ demodulation techniques

An interface for a capacitive pressure sensor which accommodates digital encoding and linearization functions has been developed. The front-end is a switched-capacitor bridge which converts the sensor capacitance into a proportional voltage. This voltage is applied to a pulse-width modulator to be compared with a model signal generated by a digital waveform synthesizer (DWS). This model-based signal processing provides the digital equivalent of the pressure being measured. For high-yield, monolithic implementation of the interface, oversampling /spl Delta/-/spl Sigma/ demodulation techniques are used for the DWS. In addition to the digital functions, offset and scale adjustment, and cross-parameter compensation are also possible with this architecture. A prototype interface is described to demonstrate these capabilities.

Model-based identification: an adaptive approach to ocean-acoustic processing

A model-based approach is developed to solve an adaptive ocean-acoustic signal-processing problem. Model-based signal processing is a well-defined methodology enabling the inclusion of propagation models, measurement models, and noise models into sophisticated processing algorithms. Here, we investigate the design of a so-called model-based identifier (MBID) for a general nonlinear state-space structure and apply it to a shallow water ocean-acoustic problem characterized by the normal-mode model. In this problem, we assume that the structure of the model is known and we show how this parameter-adaptive processor can be configured to jointly estimate both the modal functions and the horizontal wave numbers directly from the measured pressure-field and sound speed. We first design the model-based identifier using a model developed from a shallow-water ocean experiment and then apply it to a corresponding set of experimental data demonstrating the feasibility of this approach. It is also shown that one of the benefits of this adaptive approach is a solution to the so-called mismatch problem in matched-field processing (MFP).

Model-based environmental inversion: A shallow water ocean application

A model-based approach to invert or estimate the sound-speed profile (SSP) from noisy pressure-field measurements or more succinctly a solution to the model-based inversion problem is discussed. Recall that model-based signal processing is a well-defined methodology enabling the inclusion of environmental (propagation) models, measurement (sensor arrays) models and noise (shipping, measurement) models into sophisticated processing algorithms. Here the design of a model-based processor (MBP) is investigated based on a Taylor series expansion of the SSP about the most current set of parameter estimates augmented into the state-space representation of a normal-mode propagation model to solve the environmental inversion problem. The resulting processor is adaptive in terms of simultaneously estimating the sound speed, modes, and pressure field at each iteration. Using data obtained from from the well-known Hudson Canyon experiment, a noisy shallow water ocean environment, the processor is designed and the results compared to those predicted using various propagation models and data. It is shown that the on-line, MBP not only predicts the sound speed quite well, but also is able to simultaneously provide enhanced estimates of both modal and pressure-field measurements which are useful for localization and rapid ocean environmental characterization.

Passive localization in ocean acoustics: A model-based approach

A model-based approach is developed to solve the passive localization problem in ocean acoustics using the state-space formulation for the first time. It is shown that the inherent structure of the resulting processor consists of a parameter estimator coupled to a nonlinear optimization scheme. The parameter estimator is designed using the model-based approach in which an ocean acoustic propagation model is used in developing the model-based processor required for localization. Recall that model-based signal processing is a well-defined methodology enabling the inclusion of environmental (propagation) models, measurement (sensor arrays) models, and noise (shipping, measurement) models into a sophisticated processing algorithm. Here the parameter estimator is designed, or more appropriately the model-based identifier (MBID) for a propagation model developed from a shallow water ocean experiment. After simulation, it is then applied to a set of experimental data demonstrating the applicability of this approach.

Model-based multifrequency array signal processing for low-angle tracking

Tracking low-altitude targets over the sea is problematic because of interference between the direct and reflected signal. Standard monopulse trackers can experience large errors because of multipath maximum likelihood estimation (MLE) has been used to more accurately estimate the target height in the presence of multipath MLE is a model-fitting technique where the model parameters are chosen to maximize the likelihood function. It is shown that the type of observation model has a large effect on performance. Tracking performance is compared using three different observation models employing varying amounts of a priori information. Results are presented for different array sizes: eight and 32-element arrays and two-element subarrays typical of phase monopulse. Performance is compared with that of standard techniques such as Fourier beamforming and phase monopulse. >

MODEL-BASED SIGNAL ENHANCEMENT FOR THE HUDSON CANYON EXPERIMENT

Model-based signal processing is a well-defined methodology enabling the inclusion of environmental (propagation) models, measurement (sensor arrays) models, and noise (shipping, measurement) models into a sophisticated processing algorithm. Here we investigate the design of a space-varying, nonstationary, model-based processor (MBP) for the Hudson Canyon experiment. In this shallow water application, a state space representation of the normal mode propagation model is used. The processor is designed such that it allows in situ recursive estimation of the both the pressure field and modal functions. It is shown that the MBP can be effectively utilized to "validate" the performance of the model on noisy ocean acoustic data. In fact, a set of processors is designed, one for each source range, and the results are reasonable, implying that the propagation model with measured parameters adequately represents the data.

Model-based processor design for a shallow water ocean acoustic experiment

Model-based signal processing is a well-defined methodology enabling the inclusion of environmental (propagation) models, measurement (sensor arrays) models, and noise (shipping, measurement) models into a sophisticated processing algorithm. Depending on the class of model developed from the mathematical representation of the physical phenomenology, various processors can evolve. Here the design of a space-varying, nonstationary, model-based processor (MBP) is investigated and applied to the data from a well-controlled shallow water experiment performed at Hudson Canyon. This particular experiment is very attractive for the inaugural application of the MBP because it was performed in shallow water at low frequency requiring a small number of modes. In essence, the Hudson Canyon represents a well-known ocean environment, making it ideal for this investigation. In this shallow water application, a state-space representation of the normal-mode propagation model is used. The processor is designed such that it allows in situ recursive estimation of both the pressure-field and modal functions. It is shown that the MBP can be effectively utilized to ‘‘validate’’ the performance of the model on noisy ocean acoustic data. In fact, a set of processors is designed, one for each source range and the results are quite good—implying that the propagation model with measured parameters adequately represents the data.

Multichannel noise cancellation: A seismic application

The enclosed space detection problem is concerned with the detection of an individual or individuals present in an area that is not accessible visually, but only through sensors deployed about the perimeter under investigation. The design of a noise canceller coupled with model-based signal processing techniques to solve this problem using seismic measurements is discussed. We develop the model-based approach and show how it leads to a multichannel noise cancelling solution.

Application of Logic Programming in Linear System Identification

An experimental CAD system is built, demonstrating the utility of coupling numerical and logic programming in solving model identification problems. The feasibility study is performed on a software package for model based signal processing (SIG) coupled together with a standard Edinburgh PROLOG interpreter. The rule-based advisor written in PROLOG helps the identification algorithm written in the SIG command language to choose the structure of an ARMAX model that fits best a given time series sequence. Simulation experiments confirm the utility of coupling the numerical and logic programming packages for this purpose.

Underwater acoustic model-based signal processing

An underwater acoustic model-based Signal processing algorithm is presented. Its performance was evaluated via computer simulation of various test cases and compared to that predicted by theory. The model-based algorithm, which is used in conjunction with an FFT beamformer for planar arrays, computes phase weights that correct for deterministic, ocean medium, phase effects due to ray bending as a signal propagates in the inhomogeneous ocean medium whose index of refraction (sound-speed profile) is a function of depth. The performance of the model-based signal processing algorithm was evaluated in the context of an underwater acoustic communication problem in order to determine the impact of the model-based algorithm on the probability of detecting single rectangular-envelope continuous wave (CW) and linear frequency modulated (LFM) pulses as a function of the input signal-to-noise power ratio at a single element in the receive array for a given probability of false alarm. Preliminary results for various test cases show significant increases in performance for a correlator receiver.

Some solutions, some problems, and some questions

This is a slightly expanded written version of a plenary lecture given at the 20th IEEE Conference on Decision and Control, December 1981, San Diego, CA. This talk is intended to help spark discussion among researchers in decision, control and estimation about the status of research in the field and of promising new directions for research. One such direction, model-based signal processing, is treated in some depth together with an example, an object detection and estimation approach to tomographic reconstruction. Several additional topics are treated through the posing of questions which hopefully will provoke responses.